Overview

Dataset statistics

Number of variables12
Number of observations840
Missing cells6
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.9 KiB
Average record size in memory96.2 B

Variable types

Numeric10
Categorical2

Alerts

Año is highly overall correlated with + 20 Mbps - 30 Mbps and 2 other fieldsHigh correlation
+ 1 Mbps - 6 Mbps is highly overall correlated with TotalHigh correlation
+ 6 Mbps - 10 Mbps is highly overall correlated with + 10 Mbps - 20 Mbps and 3 other fieldsHigh correlation
+ 10 Mbps - 20 Mbps is highly overall correlated with + 6 Mbps - 10 Mbps and 3 other fieldsHigh correlation
+ 20 Mbps - 30 Mbps is highly overall correlated with Año and 5 other fieldsHigh correlation
+ 30 Mbps is highly overall correlated with Año and 5 other fieldsHigh correlation
OTROS is highly overall correlated with Año and 2 other fieldsHigh correlation
Total is highly overall correlated with + 1 Mbps - 6 Mbps and 5 other fieldsHigh correlation
Provincia is highly overall correlated with TotalHigh correlation
Provincia is uniformly distributedUniform
+ 512 Kbps - 1 Mbps has 50 (6.0%) zerosZeros
+ 6 Mbps - 10 Mbps has 38 (4.5%) zerosZeros
+ 10 Mbps - 20 Mbps has 71 (8.5%) zerosZeros
+ 20 Mbps - 30 Mbps has 104 (12.4%) zerosZeros
+ 30 Mbps has 112 (13.3%) zerosZeros
OTROS has 449 (53.5%) zerosZeros

Reproduction

Analysis started2023-07-09 01:26:58.493077
Analysis finished2023-07-09 01:27:22.245953
Duration23.75 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Año
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8857
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:22.423656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.528745
Coefficient of variation (CV)0.0012531656
Kurtosis-1.2039979
Mean2017.8857
Median Absolute Deviation (MAD)2
Skewness0.022512286
Sum1695024
Variance6.3945513
MonotonicityIncreasing
2023-07-08T20:27:22.580668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2014 96
11.4%
2015 96
11.4%
2016 96
11.4%
2017 96
11.4%
2018 96
11.4%
2019 96
11.4%
2020 96
11.4%
2021 96
11.4%
2022 72
8.6%
ValueCountFrequency (%)
2014 96
11.4%
2015 96
11.4%
2016 96
11.4%
2017 96
11.4%
2018 96
11.4%
2019 96
11.4%
2020 96
11.4%
2021 96
11.4%
2022 72
8.6%
ValueCountFrequency (%)
2022 72
8.6%
2021 96
11.4%
2020 96
11.4%
2019 96
11.4%
2018 96
11.4%
2017 96
11.4%
2016 96
11.4%
2015 96
11.4%
2014 96
11.4%

Trimestre
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
3
216 
2
216 
1
216 
4
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters840
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Length

2023-07-08T20:27:22.768070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T20:27:22.987302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring characters

ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring scripts

ValueCountFrequency (%)
Common 840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Provincia
Categorical

HIGH CORRELATION  UNIFORM 

Distinct24
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Buenos Aires
 
35
Capital Federal
 
35
Catamarca
 
35
Chaco
 
35
Chubut
 
35
Other values (19)
665 

Length

Max length19
Median length15
Mean length8.9166667
Min length5

Characters and Unicode

Total characters7490
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowCapital Federal
3rd rowCatamarca
4th rowChaco
5th rowChubut

Common Values

ValueCountFrequency (%)
Buenos Aires 35
 
4.2%
Capital Federal 35
 
4.2%
Catamarca 35
 
4.2%
Chaco 35
 
4.2%
Chubut 35
 
4.2%
Córdoba 35
 
4.2%
Corrientes 35
 
4.2%
Entre Ríos 35
 
4.2%
Formosa 35
 
4.2%
Jujuy 35
 
4.2%
Other values (14) 490
58.3%

Length

2023-07-08T20:27:23.175218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 70
 
5.3%
la 70
 
5.3%
del 70
 
5.3%
san 70
 
5.3%
buenos 35
 
2.6%
negro 35
 
2.6%
salta 35
 
2.6%
juan 35
 
2.6%
luis 35
 
2.6%
cruz 35
 
2.6%
Other values (24) 840
63.2%

Most occurring characters

ValueCountFrequency (%)
a 980
 
13.1%
e 595
 
7.9%
o 525
 
7.0%
490
 
6.5%
r 455
 
6.1%
n 455
 
6.1%
u 455
 
6.1%
t 350
 
4.7%
s 315
 
4.2%
i 315
 
4.2%
Other values (30) 2555
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5670
75.7%
Uppercase Letter 1330
 
17.8%
Space Separator 490
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 980
17.3%
e 595
10.5%
o 525
9.3%
r 455
8.0%
n 455
8.0%
u 455
8.0%
t 350
 
6.2%
s 315
 
5.6%
i 315
 
5.6%
l 175
 
3.1%
Other values (15) 1050
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 245
18.4%
S 210
15.8%
F 140
10.5%
L 105
7.9%
R 105
7.9%
J 70
 
5.3%
M 70
 
5.3%
E 70
 
5.3%
D 70
 
5.3%
T 70
 
5.3%
Other values (4) 175
13.2%
Space Separator
ValueCountFrequency (%)
490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7000
93.5%
Common 490
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 980
14.0%
e 595
 
8.5%
o 525
 
7.5%
r 455
 
6.5%
n 455
 
6.5%
u 455
 
6.5%
t 350
 
5.0%
s 315
 
4.5%
i 315
 
4.5%
C 245
 
3.5%
Other values (29) 2310
33.0%
Common
ValueCountFrequency (%)
490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7315
97.7%
None 175
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 980
13.4%
e 595
 
8.1%
o 525
 
7.2%
490
 
6.7%
r 455
 
6.2%
n 455
 
6.2%
u 455
 
6.2%
t 350
 
4.8%
s 315
 
4.3%
i 315
 
4.3%
Other values (26) 2380
32.5%
None
ValueCountFrequency (%)
í 70
40.0%
é 35
20.0%
ó 35
20.0%
á 35
20.0%

HASTA 512 kbps
Real number (ℝ)

Distinct371
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2086.4262
Minimum6
Maximum238920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:23.378953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9
Q136.75
median110
Q3568
95-th percentile6275.6
Maximum238920
Range238914
Interquartile range (IQR)531.25

Descriptive statistics

Standard deviation13615.218
Coefficient of variation (CV)6.525617
Kurtosis194.71888
Mean2086.4262
Median Absolute Deviation (MAD)95
Skewness13.218476
Sum1752598
Variance1.8537417 × 108
MonotonicityNot monotonic
2023-07-08T20:27:23.710037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 20
 
2.4%
18 16
 
1.9%
15 16
 
1.9%
10 15
 
1.8%
16 15
 
1.8%
67 14
 
1.7%
71 12
 
1.4%
8 12
 
1.4%
6 11
 
1.3%
26 11
 
1.3%
Other values (361) 698
83.1%
ValueCountFrequency (%)
6 11
1.3%
7 3
 
0.4%
8 12
1.4%
9 20
2.4%
10 15
1.8%
11 10
1.2%
12 5
 
0.6%
13 1
 
0.1%
14 1
 
0.1%
15 16
1.9%
ValueCountFrequency (%)
238920 1
0.1%
199768 1
0.1%
162513 1
0.1%
134673 1
0.1%
38215 1
0.1%
37821 1
0.1%
37542 2
0.2%
37193 1
0.1%
37192 1
0.1%
36939 2
0.2%

+ 512 Kbps - 1 Mbps
Real number (ℝ)

ZEROS 

Distinct633
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10142.846
Minimum0
Maximum171244
Zeros50
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:23.948249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1307
median2730.5
Q38130.25
95-th percentile57151.7
Maximum171244
Range171244
Interquartile range (IQR)7823.25

Descriptive statistics

Standard deviation22262.822
Coefficient of variation (CV)2.1949285
Kurtosis15.716648
Mean10142.846
Median Absolute Deviation (MAD)2633.5
Skewness3.7598641
Sum8519991
Variance4.9563326 × 108
MonotonicityNot monotonic
2023-07-08T20:27:24.179986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
6.0%
1 12
 
1.4%
4 10
 
1.2%
97 8
 
1.0%
285 8
 
1.0%
327 7
 
0.8%
109 7
 
0.8%
112 6
 
0.7%
909 6
 
0.7%
40 5
 
0.6%
Other values (623) 721
85.8%
ValueCountFrequency (%)
0 50
6.0%
1 12
 
1.4%
2 1
 
0.1%
3 2
 
0.2%
4 10
 
1.2%
5 3
 
0.4%
6 2
 
0.2%
8 1
 
0.1%
9 1
 
0.1%
10 3
 
0.4%
ValueCountFrequency (%)
171244 1
0.1%
162274 1
0.1%
137189 1
0.1%
133385 1
0.1%
132937 1
0.1%
128187 1
0.1%
124468 1
0.1%
123589 1
0.1%
115197 1
0.1%
112593 1
0.1%

+ 1 Mbps - 6 Mbps
Real number (ℝ)

HIGH CORRELATION 

Distinct831
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150969.97
Minimum2842
Maximum2299705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:24.446014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2842
5-th percentile11900.4
Q128539.25
median48834.5
Q386897.5
95-th percentile727863.25
Maximum2299705
Range2296863
Interquartile range (IQR)58358.25

Descriptive statistics

Standard deviation348153.84
Coefficient of variation (CV)2.3061132
Kurtosis21.09886
Mean150969.97
Median Absolute Deviation (MAD)25713
Skewness4.4592125
Sum1.2681477 × 108
Variance1.2121109 × 1011
MonotonicityNot monotonic
2023-07-08T20:27:24.885363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35409 3
 
0.4%
14014 3
 
0.4%
40285 2
 
0.2%
58588 2
 
0.2%
28600 2
 
0.2%
22409 2
 
0.2%
30727 2
 
0.2%
31900 1
 
0.1%
296798 1
 
0.1%
1177446 1
 
0.1%
Other values (821) 821
97.7%
ValueCountFrequency (%)
2842 1
0.1%
3107 1
0.1%
3179 1
0.1%
3576 1
0.1%
3678 1
0.1%
4386 1
0.1%
5018 1
0.1%
5312 1
0.1%
5366 1
0.1%
6038 1
0.1%
ValueCountFrequency (%)
2299705 1
0.1%
2288772 1
0.1%
2281524 1
0.1%
2279875 1
0.1%
2267852 1
0.1%
2266948 1
0.1%
2253197 1
0.1%
2250898 1
0.1%
2250445 1
0.1%
2214760 1
0.1%

+ 6 Mbps - 10 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct757
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37396.558
Minimum0
Maximum403575
Zeros38
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:25.088979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q12795.5
median10014
Q338534.5
95-th percentile203628.65
Maximum403575
Range403575
Interquartile range (IQR)35739

Descriptive statistics

Standard deviation65406.205
Coefficient of variation (CV)1.74899
Kurtosis8.2102567
Mean37396.558
Median Absolute Deviation (MAD)9970.5
Skewness2.7967281
Sum31413109
Variance4.2779716 × 109
MonotonicityNot monotonic
2023-07-08T20:27:25.323713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
4.5%
2 12
 
1.4%
1 4
 
0.5%
11 4
 
0.5%
15 3
 
0.4%
655 3
 
0.4%
2867 2
 
0.2%
2305 2
 
0.2%
5943 2
 
0.2%
31 2
 
0.2%
Other values (747) 768
91.4%
ValueCountFrequency (%)
0 38
4.5%
1 4
 
0.5%
2 12
 
1.4%
3 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
 
0.2%
9 2
 
0.2%
11 4
 
0.5%
15 3
 
0.4%
ValueCountFrequency (%)
403575 1
0.1%
402315 1
0.1%
393530 1
0.1%
335296 1
0.1%
331292 1
0.1%
321756 1
0.1%
311652 1
0.1%
311411 1
0.1%
307554 1
0.1%
304970 1
0.1%

+ 10 Mbps - 20 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct726
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38292.27
Minimum0
Maximum886678
Zeros71
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:25.559181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11360.5
median8553.5
Q326268.5
95-th percentile202207.55
Maximum886678
Range886678
Interquartile range (IQR)24908

Descriptive statistics

Standard deviation93996.49
Coefficient of variation (CV)2.4547119
Kurtosis27.578025
Mean38292.27
Median Absolute Deviation (MAD)8453.5
Skewness4.7682249
Sum32165507
Variance8.8353401 × 109
MonotonicityNot monotonic
2023-07-08T20:27:25.764008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
8.5%
1 5
 
0.6%
5 4
 
0.5%
10 3
 
0.4%
100 3
 
0.4%
119 3
 
0.4%
67 2
 
0.2%
206 2
 
0.2%
1292 2
 
0.2%
388 2
 
0.2%
Other values (716) 743
88.5%
ValueCountFrequency (%)
0 71
8.5%
1 5
 
0.6%
3 1
 
0.1%
4 2
 
0.2%
5 4
 
0.5%
7 1
 
0.1%
10 3
 
0.4%
12 1
 
0.1%
14 1
 
0.1%
16 2
 
0.2%
ValueCountFrequency (%)
886678 1
0.1%
816056 1
0.1%
712513 1
0.1%
676137 1
0.1%
636090 1
0.1%
577679 1
0.1%
576428 1
0.1%
573298 1
0.1%
523540 1
0.1%
487826 1
0.1%

+ 20 Mbps - 30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct583
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20988.008
Minimum0
Maximum949093
Zeros104
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:25.981837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.75
median1016.5
Q39429
95-th percentile84403.7
Maximum949093
Range949093
Interquartile range (IQR)9406.25

Descriptive statistics

Standard deviation74792.113
Coefficient of variation (CV)3.5635641
Kurtosis65.451051
Mean20988.008
Median Absolute Deviation (MAD)1016.5
Skewness7.1800616
Sum17629927
Variance5.5938602 × 109
MonotonicityNot monotonic
2023-07-08T20:27:26.231921image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
 
12.4%
1 24
 
2.9%
5 18
 
2.1%
2 13
 
1.5%
3 8
 
1.0%
4 7
 
0.8%
29 5
 
0.6%
22 5
 
0.6%
30 4
 
0.5%
41 4
 
0.5%
Other values (573) 648
77.1%
ValueCountFrequency (%)
0 104
12.4%
1 24
 
2.9%
2 13
 
1.5%
3 8
 
1.0%
4 7
 
0.8%
5 18
 
2.1%
6 3
 
0.4%
7 4
 
0.5%
8 1
 
0.1%
9 4
 
0.5%
ValueCountFrequency (%)
949093 1
0.1%
897964 1
0.1%
576859 1
0.1%
536049 1
0.1%
502275 1
0.1%
483572 1
0.1%
480237 1
0.1%
452570 1
0.1%
437662 1
0.1%
415020 1
0.1%

+ 30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct549
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79179.562
Minimum0
Maximum3618689
Zeros112
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:26.435477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median882.5
Q319660.75
95-th percentile322176.4
Maximum3618689
Range3618689
Interquartile range (IQR)19653.75

Descriptive statistics

Standard deviation342623.37
Coefficient of variation (CV)4.3271694
Kurtosis55.885917
Mean79179.562
Median Absolute Deviation (MAD)882.5
Skewness6.9897988
Sum66510832
Variance1.1739078 × 1011
MonotonicityNot monotonic
2023-07-08T20:27:26.685585image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
13.3%
2 39
 
4.6%
1 19
 
2.3%
3 15
 
1.8%
4 14
 
1.7%
10 13
 
1.5%
5 9
 
1.1%
22 8
 
1.0%
13 8
 
1.0%
9 7
 
0.8%
Other values (539) 596
71.0%
ValueCountFrequency (%)
0 112
13.3%
1 19
 
2.3%
2 39
 
4.6%
3 15
 
1.8%
4 14
 
1.7%
5 9
 
1.1%
6 1
 
0.1%
7 6
 
0.7%
8 5
 
0.6%
9 7
 
0.8%
ValueCountFrequency (%)
3618689 1
0.1%
3535757 1
0.1%
3381049 1
0.1%
3259793 1
0.1%
2482266 1
0.1%
2337604 1
0.1%
2246313 1
0.1%
2176242 1
0.1%
2085815 1
0.1%
1894466 1
0.1%

OTROS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct315
Distinct (%)37.8%
Missing6
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4968.6571
Minimum-1945
Maximum120464
Zeros449
Zeros (%)53.5%
Negative2
Negative (%)0.2%
Memory size6.7 KiB
2023-07-08T20:27:26.888730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-1945
5-th percentile0
Q10
median0
Q35015.25
95-th percentile22647.45
Maximum120464
Range122409
Interquartile range (IQR)5015.25

Descriptive statistics

Standard deviation12217.735
Coefficient of variation (CV)2.4589611
Kurtosis41.420895
Mean4968.6571
Median Absolute Deviation (MAD)0
Skewness5.5752748
Sum4143860
Variance1.4927304 × 108
MonotonicityNot monotonic
2023-07-08T20:27:27.138720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 449
53.5%
2151 6
 
0.7%
6105 5
 
0.6%
1035 5
 
0.6%
36917 3
 
0.4%
1618 3
 
0.4%
792 3
 
0.4%
698 3
 
0.4%
2680 3
 
0.4%
4500 3
 
0.4%
Other values (305) 351
41.8%
(Missing) 6
 
0.7%
ValueCountFrequency (%)
-1945 1
 
0.1%
-1 1
 
0.1%
0 449
53.5%
1 1
 
0.1%
2 2
 
0.2%
3 1
 
0.1%
19 1
 
0.1%
44 1
 
0.1%
45 1
 
0.1%
50 2
 
0.2%
ValueCountFrequency (%)
120464 1
0.1%
114182 1
0.1%
113357 1
0.1%
105818 1
0.1%
105607 1
0.1%
105477 1
0.1%
65849 1
0.1%
65821 1
0.1%
43573 1
0.1%
43438 1
0.1%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct834
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343988.81
Minimum12406
Maximum4721668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T20:27:27.357478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum12406
5-th percentile25701.75
Q152328.25
median104333
Q3177579.75
95-th percentile1417583.7
Maximum4721668
Range4709262
Interquartile range (IQR)125251.5

Descriptive statistics

Standard deviation737336.59
Coefficient of variation (CV)2.1434901
Kurtosis14.711416
Mean343988.81
Median Absolute Deviation (MAD)56426
Skewness3.7617998
Sum2.889506 × 108
Variance5.4366524 × 1011
MonotonicityNot monotonic
2023-07-08T20:27:27.576549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14029 3
 
0.4%
35710 3
 
0.4%
68538 2
 
0.2%
33772 2
 
0.2%
2705954 1
 
0.1%
83736 1
 
0.1%
752576 1
 
0.1%
3686254 1
 
0.1%
86733 1
 
0.1%
736108 1
 
0.1%
Other values (824) 824
98.1%
ValueCountFrequency (%)
12406 1
0.1%
12557 1
0.1%
12741 1
0.1%
13040 1
0.1%
13055 1
0.1%
13147 1
0.1%
13220 1
0.1%
13302 1
0.1%
13488 1
0.1%
13660 1
0.1%
ValueCountFrequency (%)
4721668 1
0.1%
4667183 1
0.1%
4555424 1
0.1%
4509157 1
0.1%
4251609 1
0.1%
4132351 1
0.1%
4060002 1
0.1%
4033261 1
0.1%
3971683 1
0.1%
3937277 1
0.1%

Interactions

2023-07-08T20:27:19.442673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:26:59.495918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:01.881796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:04.033245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:06.392729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:08.514565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:10.794827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:13.075889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:15.039766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:17.220752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:19.672919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:26:59.791232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:02.117749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:04.214374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:06.630091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:08.736632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:11.006494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:13.273870image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:15.301463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:17.441046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:19.871757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:00.026586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:02.346381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:04.449885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:06.828607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:08.979926image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:11.211540image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:13.506775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:15.510935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:17.658731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:20.113082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:00.332175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:02.602822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:04.696973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:07.067254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:09.191637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:11.413469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:13.712129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:15.740010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:17.867718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:20.303681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:00.552382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:02.794094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:04.899890image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:07.282672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:09.432757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:11.596549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:13.894372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:15.917571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:18.070845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:20.535294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:00.817843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:03.000082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:05.219024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:07.514776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:09.667121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:11.791613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:14.094136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:16.177343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:18.302069image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:20.715611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:01.037747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:03.182566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:05.430826image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:07.679213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:09.839960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:12.299348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:14.291328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:16.396768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:18.610850image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:20.933696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:01.227243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:03.391149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:05.612669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:07.881876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:10.096895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:12.455875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:14.457871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:16.569734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:18.852473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:21.167345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:01.455810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:03.629556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:05.868646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:08.130808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:10.320347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:12.638139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:14.651662image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:16.772216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:19.072613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:21.384389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:01.669029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:03.818081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:06.149817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:08.325697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:10.572066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:12.876045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:14.841217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:16.985167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T20:27:19.255797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-08T20:27:27.748420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AñoHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotalTrimestreProvincia
Año1.000-0.204-0.448-0.1370.3630.4570.6190.7110.7340.3020.0000.000
HASTA 512 kbps-0.2041.0000.4850.4230.2590.2790.2120.161-0.0600.3610.0000.250
+ 512 Kbps - 1 Mbps-0.4480.4851.0000.4520.3750.2650.085-0.038-0.2730.3060.0000.249
+ 1 Mbps - 6 Mbps-0.1370.4230.4521.0000.4380.4640.3400.326-0.0080.7630.0000.395
+ 6 Mbps - 10 Mbps0.3630.2590.3750.4381.0000.8490.7310.7090.3400.7650.0000.419
+ 10 Mbps - 20 Mbps0.4570.2790.2650.4640.8491.0000.8510.8350.3880.7860.0000.282
+ 20 Mbps - 30 Mbps0.6190.2120.0850.3400.7310.8511.0000.8450.5610.7320.0000.234
+ 30 Mbps0.7110.161-0.0380.3260.7090.8350.8451.0000.5900.7250.0000.290
OTROS0.734-0.060-0.273-0.0080.3400.3880.5610.5901.0000.3620.0000.292
Total0.3020.3610.3060.7630.7650.7860.7320.7250.3621.0000.0000.577
Trimestre0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
Provincia0.0000.2500.2490.3950.4190.2820.2340.2900.2920.5770.0001.000

Missing values

2023-07-08T20:27:21.687769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T20:27:22.104455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AñoTrimestreProvinciaHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotal
020144Buenos Aires8226132937225044520362010432299754060.02705954
120144Capital Federal26986289610382691234519577289079970.01331972
220144Catamarca218047140932910421010.025493
320144Chaco48312837421067908260012420.066060
420144Chubut1858621860392700000.068538
520144Córdoba190385362427844419473085811777820.0589873
620144Corrientes40138604218275132106090.065710
720144Entre Ríos231127801886491831835841660.0140730
820144Formosa194778301405818524734710.026208
920144Jujuy188010427197403443465000.035955
AñoTrimestreProvinciaHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotal
83020223Santa Fe46811132132709198588708975745539635220209.0887810
83120221Río Negro1161383355651885926926141443772922652.0157374
83220222Río Negro1111227333641793827168137144197224270.0159764
83320223Río Negro1111221329361786027934136294259624271.0160558
83420221Buenos Aires3159130056313382321756290127161183338104926280.04555424
83520221San Luis61643828738379802686121328589.0119844
83620223Buenos Aires2998527709290315297915267044124190361868965821.04721668
83720222Buenos Aires3063928323295238307554273954129869353575765849.04667183
83820223Capital Federal5175742343716782951946286921253105105477.01547679
83920222Capital Federal5175980360367243455372315711229254105607.01536771